Neural machine translation has recently shown impressive results [1]}, [2]}, [3]}.
However, the translation of rare words is an open problem.
The vocabulary of neural models is typically limited to 30000–50000 words, but translation is an open-vocabulary problem,
and especially for languages with productive word formation processes such as agglutination and compounding, translation models require mechanisms that go below the word level.
As an example, consider compounds such as the German Abwasser|behandlungs|anlange `sewage water treatment plant', for which a segmented, variable-length representation is intuitively more appealing than encoding the word as a fixed-length vector.
